Story#
import pandas as pd
import plotly.graph_objs as go
import plotly.express as px
df: pd.DataFrame = pd.read_csv('../data/emission.csv')
df.head()
| Area | Item | Element | Unit | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | ... | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Crop Residues | Direct emissions (N2O) | kilotonnes | 0.520 | 0.5267 | 0.8200 | 0.9988 | 0.8225 | 1.1821 | ... | 1.0321 | 1.3726 | 1.4018 | 1.4584 | 1.2424 | 1.1940 | 1.0617 | 0.8988 | 1.2176 | 1.3170 |
| 1 | Afghanistan | Crop Residues | Indirect emissions (N2O) | kilotonnes | 0.117 | 0.1185 | 0.1845 | 0.2247 | 0.1851 | 0.2660 | ... | 0.2322 | 0.3088 | 0.3154 | 0.3281 | 0.2795 | 0.2687 | 0.2389 | 0.2022 | 0.2740 | 0.2963 |
| 2 | Afghanistan | Crop Residues | Emissions (N2O) | kilotonnes | 0.637 | 0.6452 | 1.0045 | 1.2235 | 1.0075 | 1.4481 | ... | 1.2643 | 1.6815 | 1.7173 | 1.7865 | 1.5220 | 1.4627 | 1.3005 | 1.1011 | 1.4916 | 1.6133 |
| 3 | Afghanistan | Crop Residues | Emissions (CO2eq) from N2O (AR5) | kilotonnes | 168.807 | 170.9884 | 266.1975 | 324.2195 | 266.9995 | 383.7498 | ... | 335.0379 | 445.5958 | 455.0727 | 473.4174 | 403.3181 | 387.6130 | 344.6447 | 291.7838 | 395.2689 | 427.5284 |
| 4 | Afghanistan | Crop Residues | Emissions (CO2eq) (AR5) | kilotonnes | 168.807 | 170.9884 | 266.1975 | 324.2195 | 266.9995 | 383.7498 | ... | 335.0379 | 445.5958 | 455.0727 | 473.4174 | 403.3181 | 387.6130 | 344.6447 | 291.7838 | 395.2689 | 427.5284 |
5 rows × 25 columns
px.bar(df.where(df['Element'] == 'Emissions (CO2)'), x='Area', y='2000')
elem = df['Element'].value_counts()
elem
Element
Emissions (CO2eq) (AR5) 11926
Emissions (N2O) 9622
Emissions (CO2eq) from N2O (AR5) 9486
Emissions (CH4) 8310
Emissions (CO2eq) from CH4 (AR5) 8167
Emissions (CO2) 6677
Direct emissions (N2O) 1912
Indirect emissions (N2O) 1756
Emissions (CO2eq) from F-gases (AR5) 909
Name: count, dtype: int64
px.bar(df.where(df['Element'] == 'Emissions (CO2)'), x='Area', y='2001')
#px.bar(df.where(df['Area'] == 'World' ),x = 'Area', y = '2000')
where = df.where((df['Area'] == 'World') & (df['Element'] == 'Emissions (CO2)') & (df['Item'] == 'Energy')).dropna().transpose()[4:].reset_index().rename(columns={'index': 'Year', 50837: 'Emissions'})
px.bar(where, x= 'Year', y= 'Emissions')